BMC biomedical engineering
Automated phase recognition in the cardiac catheterization laboratory with deep learning
By Vijfvinkel TS, Frassini E, Weijenberg Y and 6 others.
Abstract
Background
An efficient workflow in the cardiac catheterization laboratory is important to guarantee quality of care and keep healthcare affordable. Automated surgical process monitoring of diagnostic coronary angiograms (CAGs) has the potential to increase workflow efficiency, reduce non-clinical workload, and optimize scheduling. The aim of this study is to automatically classify workflow phases of CAGs using deep learning models trained on a real-world dataset.
Methods
With an observational study we collected a dataset, composed of an integration of video recorded CAGs, clinical data, and system logging data. Every procedural phase was annotated by a medical doctor based on clinically relevant key points in time. A Scaled-YOLOv4 object detection algorithm was employed to extract features from the video images. We implemented Long short-term memory (LSTM) and LSTM-Fully convolutional network (LSTM-FCN) on the integrated time series data to classify the workflow phases. The accuracy in the classification was weighted on the duration of the phase.